Update app.py

app.py

@@ -1,195 +1,94 @@
+"""Basic Agent Evaluation Runner – GPT-4.1 edition (HF Spaces)"""
 import os
-import gradio as gr
 import requests
-import inspect
+import gradio as gr
 import pandas as pd
-from dotenv import load_dotenv
-from agent import BasicAgent  # Import the agent from agent.py
+from langchain_core.messages import HumanMessage
+from agent import build_graph
 
-# Load environment variables
-load_dotenv()
-
-# --- Constants ---
+# --- Constants ------------------------------------------------------------- #
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID")
+# --- Agent wrapper --------------------------------------------------------- #
+class BasicAgent:
+    """LangGraph agent ready for evaluation."""
+    def __init__(self):
+        print("BasicAgent initialized (using GPT-4.1).")
+        # provider="openai" di default
+        self.graph = build_graph(provider="openai")
 
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        msgs = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": msgs})
+        answer = result["messages"][-1].content
+        return answer[14:]  # strip "FINAL ANSWER: "
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+# --- Main evaluation logic ------------------------------------------------- #
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    # Verifica login
+    if not profile:
+        return "Please Login to Hugging Face with the button.", None
+    username = profile.username
+    print(f"User logged in: {username}")
+
+    # Crea agent
     try:
         agent = BasicAgent()
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    
+
+    # Determina URL spazio (link al codice)
+    space_id = os.getenv("SPACE_ID", "unknown-space")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    # --- Fetch domande ------------------------------------------------------ #
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            print("Fetched questions list is empty.")
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
+        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        resp.raise_for_status()
+        questions_data = resp.json()
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # --- Rispondi con l'agente --------------------------------------------- #
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
+        q_text = item.get("question")
         try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            ans = agent(q_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": ans})
+            results_log.append({"Task ID": task_id, "Question": q_text, "Submitted Answer": ans})
         except Exception as e:
-            print(f"Error running agent on task {task_id}: {e}")
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
+            results_log.append({"Task ID": task_id, "Question": q_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # --- Submit ------------------------------------------------------------- #
+    submission = {"username": username, "agent_code": agent_code, "answers": answers_payload}
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        resp.raise_for_status()
+        data = resp.json()
+        status = (
+            f"Submission Successful!\nUser: {data.get('username')}\n"
+            f"Overall Score: {data.get('score', 'N/A')}% "
+            f"({data.get('correct_count', '?')}/{data.get('total_attempted', '?')} correct)\n"
+            f"Message: {data.get('message', 'No message received.')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# LangGraph OpenAI Agent Evaluation")
-    gr.Markdown(
-        """
-        **Instructions:**
+        status = f"Submission Failed: {e}"
 
-        1. This agent uses LangGraph with OpenAI GPT-4 to answer questions.
-        2. Log in to your Hugging Face account using the button below.
-        3. Click 'Run Evaluation & Submit All Answers' to start the evaluation.
-
-        **Features:**
-        - Mathematical calculations
-        - Wikipedia search
-        - Academic paper search on Arxiv
-        
-        **Note:** Processing all questions may take some time. Please be patient.
-        """
-    )
+    return status, pd.DataFrame(results_log)
 
+# --- Gradio UI ------------------------------------------------------------- #
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner (GPT-4.1)")
     gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
+    status_box = gr.Textbox(lines=5, label="Run Status / Submission Result")
+    results_tbl = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    run_btn.click(fn=run_and_submit_all, outputs=[status_box, results_tbl])
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for environment variables
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-    openai_key = os.getenv("OPENAI_KEY")
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup:
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?).")
-    
-    if openai_key:
-        print("✅ OPENAI_KEY found")
-    else:
-        print("❌ OPENAI_KEY not found - Agent will not work without it!")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for LangGraph OpenAI Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)